A nonlinear system may be simulated by implementing a dynamic equation in a computer. The dynamic equation may be driven by one or more parameters. Properly chosen parameters may allow for the results of running the simulation to match experimental data obtained from measuring the dynamic behavior of the nonlinear system. A conventional technique for estimating parameters of a nonlinear system model uses an optimization algorithm for the purpose of minimizing an objective function which measures the difference between the desired state, generally based on experimental data, and a predicted state of the nonlinear system model. This approach requires a full simulation to be performed for every optimization iteration. Conventional techniques may only work for parameters which are constant, and may be incapable of properly estimating parameters if the parameters are dynamic. Further, conventional techniques may not deal with uncertainty in the nonlinear system model.